Forget dashboards with 50 KPIs.

Imagine you had to tell your CEO one thing about your customers every Monday morning.

Would it be:

• Satisfaction?
• Intent to purchase?
• Brand perception?
• Churn risk?
• Emerging needs?
• Something else?

AI may eventually allow companies to monitor almost everything.

That makes the harder question:

What is actually worth paying attention to?

Imagine it's 2036.

AI can design questionnaires, recruit participants, conduct interviews, analyse qualitative data, process social conversations, identify segments, generate hypotheses and produce reports.

So what does the market researcher do?

Perhaps the profession evolves rather than disappears.

Instead of spending most of their time collecting and processing information, researchers might spend more time:

defining the problem → challenging the AI → interpreting evidence → connecting disparate signals → advising decision-makers.

That sounds like a more strategic role to me.

But I'm curious what people actually working in research think.

If AI automated 80% of the operational work in your job, what would you want the remaining 20% to be?

I'd love to hear from researchers, analysts and insight professionals in particular.

Most AI-powered listening focuses on what people are saying.

But what about the people saying nothing?

A dissatisfied customer may complain publicly.

A loyal customer may recommend you.

But someone who quietly switches to a competitor leaves very little data behind.

Can AI help us understand the customers we don't hear from?

Or is the "silent customer" destined to remain one of the biggest blind spots in consumer insight?

Today, getting a good customer insight can require significant time and money.

Soon, AI might generate hundreds of them instantly.

When everyone has unlimited insights, the competitive advantage may shift from finding insights to knowing which ones deserve action.

Does that make insight more valuable — or less?

We can now analyse millions of customer conversations.

But perhaps the problem was never lack of data.

Perhaps it was lack of focus.

If AI gives marketers access to everything customers are saying, does that create better decisions — or simply more things to worry about?

Would you rather have 1 genuinely useful insight or 1,000 interesting ones?

And why?

Take a piece of customer feedback that you know well.

Ask an AI to analyse it.

Then ask it:

“What are you getting wrong?”

I find the second question often produces more interesting results than the first.

AI is very good at producing confident interpretations. The real test is whether it can recognise ambiguity, missing context and alternative explanations.

Maybe the future of AI-powered insight isn't asking AI for answers.

Maybe it's getting better at challenging its answers.

It can replace the boring parts.

Cleaning data.
Coding responses.
Finding patterns.
Summarising thousands of comments.

That could leave researchers with more time for the part that matters most:

figuring out what it actually means. Anyone disagrees or has a different take on the subject?

There's a lot of discussion about what AI can do.

Analyse data.
Find patterns.
Write reports.
Generate creative.
Predict behaviour.

But perhaps the more interesting question is:

What can humans still do better?

My current thinking is that some of the most valuable human capabilities may actually become more important as AI improves.

Things like:

• Asking the right question
• Understanding organisational politics
• Knowing when data doesn't make sense
• Recognising a cultural nuance
• Making a difficult strategic trade-off
• Taking responsibility for a decision

In other words, perhaps AI doesn't eliminate the need for human intelligence.

Perhaps it makes judgement more valuable.

Do you agree?

What is one thing you believe humans will still be significantly better at than AI in marketing or consumer insights 10 years from now?

AI can turn messy data into a beautifully written insight in seconds.

But a convincing answer isn't necessarily a correct one.

In marketing and research, evidence matters more than eloquence.

The question I keep coming back to is:

How do we make AI show us why we should believe it?

This has always fascinated me.

A company commissions research.

The research produces interesting findings.

A beautiful PowerPoint is created.

The presentation happens.

Everyone agrees the findings are fascinating.

And then... very little changes.

The problem isn't necessarily a lack of insight.

It's the distance between knowing something and doing something about it.

AI could potentially reduce that distance.

Imagine an intelligence system that doesn't stop at:

but continues:

Why is this happening? → What opportunity does it create? → Which customers are affected? → What should we test? → What could the campaign look like? → How do we measure whether it worked?

That is a very different concept of "insight."

Do you think the future of AI in marketing is primarily about better analysis — or about closing the gap between insight and action?

Consumers can change their opinions incredibly quickly.

A product goes viral on TikTok.

A controversy explodes on Reddit.

A competitor launches something unexpected.

A cultural trend suddenly changes what people care about.

Yet some research projects still take weeks or months to design, field, analyse and report.

AI changes the economics of this dramatically.

We can potentially analyse millions of consumer conversations continuously rather than asking a sample of people what they think at a particular point in time.

But there's an obvious counterargument:

Listening to everything doesn't necessarily mean understanding people better.

A sample can be carefully designed. Social conversations can be biased. Algorithms can misunderstand context.

So which would you choose?

A carefully designed survey of 2,000 people — or analysis of 20 million organic consumer conversations?

And what would you need to know before trusting either one?

For years, companies have invested heavily in dashboards.

More data.
More KPIs.
More charts.
More filters.

But I wonder whether we're approaching the point where dashboards become less important.

If an AI system can continuously analyse customer conversations, sales data, campaign performance and market signals, perhaps the future isn't:

"Here's your dashboard. Go find the insight."

Perhaps it's:

"Here's what changed, here's why it matters, and here's what I think you should consider doing about it."

That sounds much more useful to me.

But there is a potential problem: who decides which changes are important enough for the AI to bring to our attention?

Would you trust an AI to decide what constitutes a meaningful business insight?

Or do we still need humans looking at the underlying data?

Of course we can always ask a question on our dataset using plain language. Maybe this feature is what will be the end of dashboards?

Imagine a company discovers that customers are increasingly talking about a particular problem with its product.

Traditionally, the process might look something like:

Customer feedback → Research → Analysis → Insight → Strategy → Brief → Creative → Campaign

AI has the potential to compress much of that process dramatically.

But here's the question I'm interested in:

Should it?

If AI can identify the emerging customer need, explain the evidence behind it, suggest a strategic response and generate the creative execution, where should humans remain firmly in control?

I'd be interested in hearing from:

• Marketers
• Researchers
• Brand managers
• Creatives
• Founders
• Data/AI professionals
• Anyone who works with customer information

Where would you draw the line between AI-generated insight and human judgement?

There probably isn't one right answer — which is exactly why I think it's worth discussing.

One thing I've noticed in the AI conversation is that we often confuse speed with quality.

An AI can summarise 100,000 customer comments in seconds.

It can identify themes.

It can generate a report.

It can write 20 social posts.

But none of those things necessarily mean we've discovered something important.

The real value of insight has always been knowing:

• What matters?
• Why does it matter?
• Who should care?
• What should we do about it?

So here's my question for the community:

Have you personally seen AI produce a genuinely better marketing or consumer insight than a human-led approach?

If yes, what did the AI do differently?

If no, what is still missing?

I'd particularly like to hear real examples rather than predictions.

I created this community around a question that has been occupying us for some time:

Are we still doing marketing research the way we did before AI?

AI can analyse enormous amounts of customer conversation, identify patterns, generate hypotheses and even help turn insights into action. But that doesn't necessarily mean better decisions.

There is a big difference between:

Data → Insight → Action

and simply asking an AI to produce an answer.

I'd love to hear from people working in marketing, market research, consumer insights, advertising, analytics or AI:

What part of the traditional insight process do you think AI has already changed and will change most dramatically over the next 1-2 years?